/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    NBNodeAdaptive.java
 *    Copyright (C) 2013 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.trees.ht;

import java.io.Serializable;
import java.util.Map;

import weka.core.Attribute;
import weka.core.Instance;
import weka.core.Instances;

/**
 * Implements a LearningNode that chooses between using majority class or naive
 * Bayes for prediction
 * 
 * @author Richard Kirkby (rkirkby@cs.waikato.ac.nz)
 * @author Mark Hall (mhall{[at]}pentaho{[dot]}com)
 * @version $Revision$
 */
public class NBNodeAdaptive extends NBNode implements LearningNode, Serializable {

    /**
     * For serialization
     */
    private static final long serialVersionUID = -4509802312019989686L;

    /** The number of correct predictions made by the majority class */
    protected double m_majClassCorrectWeight = 0;

    /** The number of correct predictions made by naive Bayes */
    protected double m_nbCorrectWeight = 0;

    /**
     * Constructor
     * 
     * @param header            the structure of the instances we're training from
     * @param nbWeightThreshold the weight mass to see before allowing naive Bayes
     *                          to predict
     * @throws Exception if a problem occurs
     */
    public NBNodeAdaptive(Instances header, double nbWeightThreshold) throws Exception {
        super(header, nbWeightThreshold);
    }

    protected String majorityClass() {
        String mc = "";
        double max = -1;

        for (Map.Entry<String, WeightMass> e : m_classDistribution.entrySet()) {
            if (e.getValue().m_weight > max) {
                max = e.getValue().m_weight;
                mc = e.getKey();
            }
        }

        return mc;
    }

    @Override
    public void updateNode(Instance inst) throws Exception {

        String trueClass = inst.classAttribute().value((int) inst.classValue());
        int trueClassIndex = (int) inst.classValue();

        if (majorityClass().equals(trueClass)) {
            m_majClassCorrectWeight += inst.weight();
        }

        if (m_bayes.classifyInstance(inst) == trueClassIndex) {
            m_nbCorrectWeight += inst.weight();
        }

        super.updateNode(inst);
    }

    @Override
    public double[] getDistribution(Instance inst, Attribute classAtt) throws Exception {

        if (m_majClassCorrectWeight > m_nbCorrectWeight) {
            return super.bypassNB(inst, classAtt);
        }

        return super.getDistribution(inst, classAtt);
    }

    @Override
    protected int dumpTree(int depth, int leafCount, StringBuffer buff) {
        leafCount = super.dumpTree(depth, leafCount, buff);

        buff.append(" NB adaptive" + m_leafNum);

        return leafCount;
    }

    @Override
    protected void printLeafModels(StringBuffer buff) {
        buff.append("NB adaptive" + m_leafNum).append("\n").append(m_bayes.toString());
    }

}
